code: def _parallel_approx_estimators(
      n_estimators,
      clfs,
      X,
      total_n_estimators,
      approx_flags,
      approximator,
      rp_transformers,
      verbose,
  ):
      """
      Parameters
      ----------
      n_estimators
      clfs
      X
      total_n_estimators
      approx_flags
      approximator
      verbose
  
      Returns
      -------
  
      """
      X = check_array(X)
      # Build estimators
      approximators = []
  
      # TODO: approximators can be different
      for i in range(n_estimators):
          # project matrix
          X_scaled = jl_transform(X, rp_transformers[i])
  
          estimator = clfs[i]
  
          check_is_fitted(estimator, ["decision_scores_"])
          if verbose > 1:
              print(
                  "Building estimator %d of %d for this parallel run "
                  "(total %d)..."
                  % (i + 1, n_estimators, total_n_estimators)
              )
  
          if approx_flags[i] == 1:
              # operate on the reduce space
              pseudo_scores = estimator.decision_scores_
              # pseudo_scores = estimator.decision_function(X)
              # use the same type of approximator for all models
              base_approximater = clone(approximator)
              base_approximater.fit(X_scaled, pseudo_scores)
  
              approximators.append(base_approximater)
  
          else:
              approximators.append(None)
  
      return approximators
  
file_path: SUOD/suod/models/parallel_processes.py
imports: from sklearn.base import clone
  import numpy
  from scipy.stats import rankdata
  from joblib import effective_n_jobs
  from sklearn.utils import check_array
  from sklearn.utils.validation import check_is_fitted
  from pyod.utils.utility import score_to_label
  from .jl_projection import jl_fit_transform, jl_transform
  from ..utils.utility import raw_score_to_proba
label_desc: Use image node_id 9 for calling a global function with example usage: _parallel_approx_estimators(n_estimators, clfs, X, total_n_estimators, approx_flags, approximator, rp_transformers, verbose) and returns: approximators